Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations870
Missing cells4565
Missing cells (%)22.8%
Duplicate rows10
Duplicate rows (%)1.1%
Total size in memory416.0 KiB
Average record size in memory489.6 B

Variable types

Text1
Numeric8
Categorical14

Alerts

Dataset has 10 (1.1%) duplicate rowsDuplicates
construction year has a high cardinality: 111 distinct values High cardinality
asbestos certificate is highly overall correlated with primary energy consumptionHigh correlation
bathrooms is highly overall correlated with bedroomsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
energy class is highly overall correlated with primary energy consumptionHigh correlation
inspection report of the electrical installation is highly overall correlated with primary energy consumptionHigh correlation
living area is highly overall correlated with bedrooms and 2 other fieldsHigh correlation
price is highly overall correlated with living area and 2 other fieldsHigh correlation
primary energy consumption is highly overall correlated with asbestos certificate and 3 other fieldsHigh correlation
sewer network connection is highly overall correlated with primary energy consumptionHigh correlation
surface of the plot is highly overall correlated with priceHigh correlation
toilets is highly overall correlated with bedrooms and 2 other fieldsHigh correlation
inspection report of the electrical installation is highly imbalanced (60.6%) Imbalance
shared building is highly imbalanced (65.4%) Imbalance
sewer network connection is highly imbalanced (84.0%) Imbalance
proceedings for breach of planning regulations is highly imbalanced (98.1%) Imbalance
double glazing is highly imbalanced (76.3%) Imbalance
construction year has 347 (39.9%) missing values Missing
building condition has 184 (21.1%) missing values Missing
asbestos certificate has 309 (35.5%) missing values Missing
living area has 121 (13.9%) missing values Missing
bedrooms has 97 (11.1%) missing values Missing
bathrooms has 123 (14.1%) missing values Missing
toilets has 231 (26.6%) missing values Missing
primary energy consumption has 266 (30.6%) missing values Missing
energy class has 209 (24.0%) missing values Missing
inspection report of the electrical installation has 329 (37.8%) missing values Missing
subdivision permit has 299 (34.4%) missing values Missing
possible priority purchase right has 237 (27.2%) missing values Missing
g-score has 245 (28.2%) missing values Missing
shared building has 83 (9.5%) missing values Missing
surface of the plot has 216 (24.8%) missing values Missing
sewer network connection has 398 (45.7%) missing values Missing
proceedings for breach of planning regulations has 327 (37.6%) missing values Missing
designated land use has 239 (27.5%) missing values Missing
double glazing has 305 (35.1%) missing values Missing
primary energy consumption is highly skewed (γ1 = 24.50855464) Skewed

Reproduction

Analysis started2025-06-21 01:31:50.559150
Analysis finished2025-06-21 01:31:55.032481
Duration4.47 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct815
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size331.1 KiB
2025-06-21T03:31:55.153959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length92
Median length78
Mean length57.904598
Min length41

Characters and Unicode

Total characters50377
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique777 ?
Unique (%)89.3%

Sample

1st rowvrijdagmarkt 61 box 305 9000 — gent
2nd rowgitsestraat 545 8800 — roeselare
3rd rowbruggestraat 143 8730 — beernem
4th row8670 — koksijde ask for the exact address
5th rowhamerstraat 75 9000 — gent
ValueCountFrequency (%)
870
 
17.3%
box 98
 
1.9%
ask 95
 
1.9%
for 95
 
1.9%
the 95
 
1.9%
exact 95
 
1.9%
address 95
 
1.9%
gent 71
 
1.4%
9000 53
 
1.1%
9500 29
 
0.6%
Other values (1288) 3443
68.3%
2025-06-21T03:31:55.290314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24007
47.7%
e 3429
 
6.8%
a 2321
 
4.6%
t 1943
 
3.9%
r 1828
 
3.6%
s 1571
 
3.1%
0 1405
 
2.8%
n 1154
 
2.3%
o 973
 
1.9%
870
 
1.7%
Other values (38) 10876
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
24007
47.7%
e 3429
 
6.8%
a 2321
 
4.6%
t 1943
 
3.9%
r 1828
 
3.6%
s 1571
 
3.1%
0 1405
 
2.8%
n 1154
 
2.3%
o 973
 
1.9%
870
 
1.7%
Other values (38) 10876
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
24007
47.7%
e 3429
 
6.8%
a 2321
 
4.6%
t 1943
 
3.9%
r 1828
 
3.6%
s 1571
 
3.1%
0 1405
 
2.8%
n 1154
 
2.3%
o 973
 
1.9%
870
 
1.7%
Other values (38) 10876
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
24007
47.7%
e 3429
 
6.8%
a 2321
 
4.6%
t 1943
 
3.9%
r 1828
 
3.6%
s 1571
 
3.1%
0 1405
 
2.8%
n 1154
 
2.3%
o 973
 
1.9%
870
 
1.7%
Other values (38) 10876
21.6%

price
Real number (ℝ)

High correlation 

Distinct408
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449736.17
Minimum38500
Maximum2300000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.1 KiB
2025-06-21T03:31:55.331239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum38500
5-th percentile171800
Q1295000
median396974
Q3534902
95-th percentile887750
Maximum2300000
Range2261500
Interquartile range (IQR)239902

Descriptive statistics

Standard deviation255129.35
Coefficient of variation (CV)0.5672867
Kurtosis10.462038
Mean449736.17
Median Absolute Deviation (MAD)111974
Skewness2.482359
Sum3.9127047 × 108
Variance6.5090985 × 1010
MonotonicityNot monotonic
2025-06-21T03:31:55.366222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
299000 23
 
2.6%
349000 16
 
1.8%
249000 14
 
1.6%
199000 12
 
1.4%
399000 12
 
1.4%
375000 11
 
1.3%
335000 9
 
1.0%
495000 9
 
1.0%
449000 8
 
0.9%
475000 8
 
0.9%
Other values (398) 748
86.0%
ValueCountFrequency (%)
38500 1
 
0.1%
85000 1
 
0.1%
99000 1
 
0.1%
100000 1
 
0.1%
109000 1
 
0.1%
115000 3
0.3%
120000 2
0.2%
125000 2
0.2%
129000 1
 
0.1%
138000 1
 
0.1%
ValueCountFrequency (%)
2300000 1
0.1%
2275000 1
0.1%
1950000 1
0.1%
1750000 1
0.1%
1650000 1
0.1%
1595000 1
0.1%
1575000 1
0.1%
1550000 1
0.1%
1500000 1
0.1%
1499000 1
0.1%

construction year
Categorical

High cardinality  Missing 

Distinct111
Distinct (%)21.2%
Missing347
Missing (%)39.9%
Memory size117.1 KiB
2024.0
 
30
1930.0
 
18
2023.0
 
13
2025.0
 
12
1918.0
 
12
Other values (106)
438 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters3138
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)3.3%

Sample

1st row2015.0
2nd row1974.0
3rd row2022.0
4th row1899.0
5th row2023.0

Common Values

ValueCountFrequency (%)
2024.0 30
 
3.4%
1930.0 18
 
2.1%
2023.0 13
 
1.5%
2025.0 12
 
1.4%
1918.0 12
 
1.4%
1850.0 10
 
1.1%
1969.0 10
 
1.1%
2027.0 9
 
1.0%
1977.0 9
 
1.0%
2021.0 8
 
0.9%
Other values (101) 392
45.1%
(Missing) 347
39.9%

Length

2025-06-21T03:31:55.396518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024.0 30
 
5.7%
1930.0 18
 
3.4%
2023.0 13
 
2.5%
1918.0 12
 
2.3%
2025.0 12
 
2.3%
1850.0 10
 
1.9%
1969.0 10
 
1.9%
2027.0 9
 
1.7%
1977.0 9
 
1.7%
1950.0 8
 
1.5%
Other values (101) 392
75.0%

Most occurring characters

ValueCountFrequency (%)
0 803
25.6%
. 523
16.7%
1 457
14.6%
9 449
14.3%
2 278
 
8.9%
5 115
 
3.7%
8 110
 
3.5%
3 106
 
3.4%
7 106
 
3.4%
6 103
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 803
25.6%
. 523
16.7%
1 457
14.6%
9 449
14.3%
2 278
 
8.9%
5 115
 
3.7%
8 110
 
3.5%
3 106
 
3.4%
7 106
 
3.4%
6 103
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 803
25.6%
. 523
16.7%
1 457
14.6%
9 449
14.3%
2 278
 
8.9%
5 115
 
3.7%
8 110
 
3.5%
3 106
 
3.4%
7 106
 
3.4%
6 103
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 803
25.6%
. 523
16.7%
1 457
14.6%
9 449
14.3%
2 278
 
8.9%
5 115
 
3.7%
8 110
 
3.5%
3 106
 
3.4%
7 106
 
3.4%
6 103
 
3.3%

building condition
Categorical

Missing 

Distinct6
Distinct (%)0.9%
Missing184
Missing (%)21.1%
Memory size112.4 KiB
3.0
379 
1.0
118 
4.0
90 
5.0
56 
2.0
 
37

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2058
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 379
43.6%
1.0 118
 
13.6%
4.0 90
 
10.3%
5.0 56
 
6.4%
2.0 37
 
4.3%
6.0 6
 
0.7%
(Missing) 184
21.1%

Length

2025-06-21T03:31:55.421045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T03:31:55.444390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0 379
55.2%
1.0 118
 
17.2%
4.0 90
 
13.1%
5.0 56
 
8.2%
2.0 37
 
5.4%
6.0 6
 
0.9%

Most occurring characters

ValueCountFrequency (%)
. 686
33.3%
0 686
33.3%
3 379
18.4%
1 118
 
5.7%
4 90
 
4.4%
5 56
 
2.7%
2 37
 
1.8%
6 6
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 686
33.3%
0 686
33.3%
3 379
18.4%
1 118
 
5.7%
4 90
 
4.4%
5 56
 
2.7%
2 37
 
1.8%
6 6
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 686
33.3%
0 686
33.3%
3 379
18.4%
1 118
 
5.7%
4 90
 
4.4%
5 56
 
2.7%
2 37
 
1.8%
6 6
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 686
33.3%
0 686
33.3%
3 379
18.4%
1 118
 
5.7%
4 90
 
4.4%
5 56
 
2.7%
2 37
 
1.8%
6 6
 
0.3%

asbestos certificate
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.4%
Missing309
Missing (%)35.5%
Memory size112.3 KiB
1.0
306 
0.0
255 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1683
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 306
35.2%
0.0 255
29.3%
(Missing) 309
35.5%

Length

2025-06-21T03:31:55.470388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T03:31:55.486113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 306
54.5%
0.0 255
45.5%

Most occurring characters

ValueCountFrequency (%)
0 816
48.5%
. 561
33.3%
1 306
 
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1683
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 816
48.5%
. 561
33.3%
1 306
 
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1683
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 816
48.5%
. 561
33.3%
1 306
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1683
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 816
48.5%
. 561
33.3%
1 306
 
18.2%

living area
Real number (ℝ)

High correlation  Missing 

Distinct279
Distinct (%)37.2%
Missing121
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean195.15487
Minimum25
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.1 KiB
2025-06-21T03:31:55.511025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile81
Q1128
median173
Q3225
95-th percentile370
Maximum2500
Range2475
Interquartile range (IQR)97

Descriptive statistics

Standard deviation134.09258
Coefficient of variation (CV)0.68710856
Kurtosis120.70551
Mean195.15487
Median Absolute Deviation (MAD)47
Skewness8.0669629
Sum146171
Variance17980.821
MonotonicityNot monotonic
2025-06-21T03:31:55.544299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180 14
 
1.6%
170 11
 
1.3%
150 11
 
1.3%
165 10
 
1.1%
174 10
 
1.1%
220 8
 
0.9%
195 8
 
0.9%
100 8
 
0.9%
161 8
 
0.9%
210 8
 
0.9%
Other values (269) 653
75.1%
(Missing) 121
 
13.9%
ValueCountFrequency (%)
25 1
0.1%
30 1
0.1%
34 1
0.1%
38 1
0.1%
40 1
0.1%
41 1
0.1%
45 1
0.1%
46 1
0.1%
50 1
0.1%
52 1
0.1%
ValueCountFrequency (%)
2500 1
0.1%
992 2
0.2%
840 1
0.1%
791 1
0.1%
713 1
0.1%
654 1
0.1%
622 1
0.1%
620 1
0.1%
579 1
0.1%
541 1
0.1%

bedrooms
Categorical

High correlation  Missing 

Distinct11
Distinct (%)1.4%
Missing97
Missing (%)11.1%
Memory size112.5 KiB
3.0
366 
4.0
159 
2.0
143 
5.0
57 
1.0
 
25
Other values (6)
 
23

Length

Max length4
Median length3
Mean length3.003881
Min length3

Characters and Unicode

Total characters2322
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.4%

Sample

1st row2.0
2nd row5.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 366
42.1%
4.0 159
18.3%
2.0 143
 
16.4%
5.0 57
 
6.6%
1.0 25
 
2.9%
6.0 13
 
1.5%
7.0 5
 
0.6%
8.0 2
 
0.2%
10.0 1
 
0.1%
14.0 1
 
0.1%
(Missing) 97
 
11.1%

Length

2025-06-21T03:31:55.572222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3.0 366
47.3%
4.0 159
20.6%
2.0 143
 
18.5%
5.0 57
 
7.4%
1.0 25
 
3.2%
6.0 13
 
1.7%
7.0 5
 
0.6%
8.0 2
 
0.3%
10.0 1
 
0.1%
14.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 774
33.3%
. 773
33.3%
3 366
15.8%
4 161
 
6.9%
2 144
 
6.2%
5 57
 
2.5%
1 27
 
1.2%
6 13
 
0.6%
7 5
 
0.2%
8 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 774
33.3%
. 773
33.3%
3 366
15.8%
4 161
 
6.9%
2 144
 
6.2%
5 57
 
2.5%
1 27
 
1.2%
6 13
 
0.6%
7 5
 
0.2%
8 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 774
33.3%
. 773
33.3%
3 366
15.8%
4 161
 
6.9%
2 144
 
6.2%
5 57
 
2.5%
1 27
 
1.2%
6 13
 
0.6%
7 5
 
0.2%
8 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 774
33.3%
. 773
33.3%
3 366
15.8%
4 161
 
6.9%
2 144
 
6.2%
5 57
 
2.5%
1 27
 
1.2%
6 13
 
0.6%
7 5
 
0.2%
8 2
 
0.1%

bathrooms
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)1.1%
Missing123
Missing (%)14.1%
Infinite0
Infinite (%)0.0%
Mean1.2690763
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.1 KiB
2025-06-21T03:31:55.591746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum14
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.78773751
Coefficient of variation (CV)0.62071721
Kurtosis102.63916
Mean1.2690763
Median Absolute Deviation (MAD)0
Skewness7.9156692
Sum948
Variance0.62053038
MonotonicityNot monotonic
2025-06-21T03:31:55.615292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 601
69.1%
2 121
 
13.9%
3 15
 
1.7%
4 4
 
0.5%
6 2
 
0.2%
5 2
 
0.2%
8 1
 
0.1%
14 1
 
0.1%
(Missing) 123
 
14.1%
ValueCountFrequency (%)
1 601
69.1%
2 121
 
13.9%
3 15
 
1.7%
4 4
 
0.5%
5 2
 
0.2%
6 2
 
0.2%
8 1
 
0.1%
14 1
 
0.1%
ValueCountFrequency (%)
14 1
 
0.1%
8 1
 
0.1%
6 2
 
0.2%
5 2
 
0.2%
4 4
 
0.5%
3 15
 
1.7%
2 121
 
13.9%
1 601
69.1%

toilets
Real number (ℝ)

High correlation  Missing 

Distinct7
Distinct (%)1.1%
Missing231
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean1.8262911
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.1 KiB
2025-06-21T03:31:55.636283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum14
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.87073484
Coefficient of variation (CV)0.47677769
Kurtosis61.049102
Mean1.8262911
Median Absolute Deviation (MAD)0
Skewness5.0597025
Sum1167
Variance0.75817917
MonotonicityNot monotonic
2025-06-21T03:31:55.659107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 372
42.8%
1 207
23.8%
3 42
 
4.8%
4 10
 
1.1%
5 6
 
0.7%
6 1
 
0.1%
14 1
 
0.1%
(Missing) 231
26.6%
ValueCountFrequency (%)
1 207
23.8%
2 372
42.8%
3 42
 
4.8%
4 10
 
1.1%
5 6
 
0.7%
6 1
 
0.1%
14 1
 
0.1%
ValueCountFrequency (%)
14 1
 
0.1%
6 1
 
0.1%
5 6
 
0.7%
4 10
 
1.1%
3 42
 
4.8%
2 372
42.8%
1 207
23.8%

primary energy consumption
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct394
Distinct (%)65.2%
Missing266
Missing (%)30.6%
Infinite0
Infinite (%)0.0%
Mean590.79636
Minimum1
Maximum151094
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.1 KiB
2025-06-21T03:31:55.688236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41.6
Q1163
median275
Q3445.5
95-th percentile871.55
Maximum151094
Range151093
Interquartile range (IQR)282.5

Descriptive statistics

Standard deviation6139.6949
Coefficient of variation (CV)10.392236
Kurtosis601.76842
Mean590.79636
Median Absolute Deviation (MAD)127
Skewness24.508555
Sum356841
Variance37695854
MonotonicityNot monotonic
2025-06-21T03:31:55.725338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225 6
 
0.7%
100 5
 
0.6%
148 5
 
0.6%
203 4
 
0.5%
298 4
 
0.5%
193 4
 
0.5%
532 4
 
0.5%
415 4
 
0.5%
155 4
 
0.5%
358 4
 
0.5%
Other values (384) 560
64.4%
(Missing) 266
30.6%
ValueCountFrequency (%)
1 2
0.2%
3 2
0.2%
8 2
0.2%
9 1
 
0.1%
10 2
0.2%
16 1
 
0.1%
19 3
0.3%
20 3
0.3%
22 2
0.2%
26 1
 
0.1%
ValueCountFrequency (%)
151094 1
0.1%
2523 1
0.1%
1446 2
0.2%
1377 1
0.1%
1191 1
0.1%
1150 1
0.1%
1130 1
0.1%
1122 1
0.1%
1107 2
0.2%
1079 1
0.1%

energy class
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)1.2%
Missing209
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean5.3751891
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.1 KiB
2025-06-21T03:31:55.751975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.729842
Coefficient of variation (CV)0.32181974
Kurtosis-1.0856268
Mean5.3751891
Median Absolute Deviation (MAD)1
Skewness0.15330126
Sum3553
Variance2.9923532
MonotonicityNot monotonic
2025-06-21T03:31:55.774523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 139
16.0%
5 130
14.9%
8 125
14.4%
6 107
12.3%
3 91
10.5%
7 59
 
6.8%
2 9
 
1.0%
1 1
 
0.1%
(Missing) 209
24.0%
ValueCountFrequency (%)
1 1
 
0.1%
2 9
 
1.0%
3 91
10.5%
4 139
16.0%
5 130
14.9%
6 107
12.3%
7 59
6.8%
8 125
14.4%
ValueCountFrequency (%)
8 125
14.4%
7 59
6.8%
6 107
12.3%
5 130
14.9%
4 139
16.0%
3 91
10.5%
2 9
 
1.0%
1 1
 
0.1%

inspection report of the electrical installation
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.4%
Missing329
Missing (%)37.8%
Memory size112.3 KiB
1.0
499 
0.0
 
42

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1623
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 499
57.4%
0.0 42
 
4.8%
(Missing) 329
37.8%

Length

2025-06-21T03:31:55.801316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T03:31:55.817787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 499
92.2%
0.0 42
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0 583
35.9%
. 541
33.3%
1 499
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1623
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 583
35.9%
. 541
33.3%
1 499
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1623
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 583
35.9%
. 541
33.3%
1 499
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1623
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 583
35.9%
. 541
33.3%
1 499
30.7%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size112.3 KiB
1
521 
0
349 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters870
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 521
59.9%
0 349
40.1%

Length

2025-06-21T03:31:55.836146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T03:31:55.851337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 521
59.9%
0 349
40.1%

Most occurring characters

ValueCountFrequency (%)
1 521
59.9%
0 349
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 870
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 521
59.9%
0 349
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 870
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 521
59.9%
0 349
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 870
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 521
59.9%
0 349
40.1%

subdivision permit
Categorical

Missing 

Distinct2
Distinct (%)0.4%
Missing299
Missing (%)34.4%
Memory size112.3 KiB
0.0
419 
1.0
152 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1713
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 419
48.2%
1.0 152
 
17.5%
(Missing) 299
34.4%

Length

2025-06-21T03:31:55.873097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T03:31:55.889725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 419
73.4%
1.0 152
 
26.6%

Most occurring characters

ValueCountFrequency (%)
0 990
57.8%
. 571
33.3%
1 152
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 990
57.8%
. 571
33.3%
1 152
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 990
57.8%
. 571
33.3%
1 152
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 990
57.8%
. 571
33.3%
1 152
 
8.9%
Distinct2
Distinct (%)0.3%
Missing237
Missing (%)27.2%
Memory size112.3 KiB
0.0
544 
1.0
89 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1899
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 544
62.5%
1.0 89
 
10.2%
(Missing) 237
27.2%

Length

2025-06-21T03:31:55.911263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T03:31:55.927208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 544
85.9%
1.0 89
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0 1177
62.0%
. 633
33.3%
1 89
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1899
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1177
62.0%
. 633
33.3%
1 89
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1899
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1177
62.0%
. 633
33.3%
1 89
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1899
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1177
62.0%
. 633
33.3%
1 89
 
4.7%

non-flood zone
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size112.3 KiB
1
588 
0
282 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters870
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 588
67.6%
0 282
32.4%

Length

2025-06-21T03:31:55.948331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T03:31:55.964390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 588
67.6%
0 282
32.4%

Most occurring characters

ValueCountFrequency (%)
1 588
67.6%
0 282
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 870
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 588
67.6%
0 282
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 870
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 588
67.6%
0 282
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 870
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 588
67.6%
0 282
32.4%

g-score
Categorical

Missing 

Distinct4
Distinct (%)0.6%
Missing245
Missing (%)28.2%
Memory size112.4 KiB
1.0
467 
4.0
55 
2.0
54 
3.0
49 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1875
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 467
53.7%
4.0 55
 
6.3%
2.0 54
 
6.2%
3.0 49
 
5.6%
(Missing) 245
28.2%

Length

2025-06-21T03:31:55.986698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T03:31:56.005051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 467
74.7%
4.0 55
 
8.8%
2.0 54
 
8.6%
3.0 49
 
7.8%

Most occurring characters

ValueCountFrequency (%)
. 625
33.3%
0 625
33.3%
1 467
24.9%
4 55
 
2.9%
2 54
 
2.9%
3 49
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 625
33.3%
0 625
33.3%
1 467
24.9%
4 55
 
2.9%
2 54
 
2.9%
3 49
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 625
33.3%
0 625
33.3%
1 467
24.9%
4 55
 
2.9%
2 54
 
2.9%
3 49
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 625
33.3%
0 625
33.3%
1 467
24.9%
4 55
 
2.9%
2 54
 
2.9%
3 49
 
2.6%

shared building
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)0.3%
Missing83
Missing (%)9.5%
Memory size112.3 KiB
0.0
736 
1.0
 
51

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2361
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 736
84.6%
1.0 51
 
5.9%
(Missing) 83
 
9.5%

Length

2025-06-21T03:31:56.029038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T03:31:56.043757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 736
93.5%
1.0 51
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 1523
64.5%
. 787
33.3%
1 51
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2361
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1523
64.5%
. 787
33.3%
1 51
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2361
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1523
64.5%
. 787
33.3%
1 51
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2361
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1523
64.5%
. 787
33.3%
1 51
 
2.2%

surface of the plot
Real number (ℝ)

High correlation  Missing 

Distinct497
Distinct (%)76.0%
Missing216
Missing (%)24.8%
Infinite0
Infinite (%)0.0%
Mean936.83639
Minimum38
Maximum54626
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.1 KiB
2025-06-21T03:31:56.069835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile87.3
Q1236
median488
Q3915.75
95-th percentile2614.75
Maximum54626
Range54588
Interquartile range (IQR)679.75

Descriptive statistics

Standard deviation2501.4105
Coefficient of variation (CV)2.6700612
Kurtosis328.12742
Mean936.83639
Median Absolute Deviation (MAD)299.5
Skewness15.986032
Sum612691
Variance6257054.4
MonotonicityNot monotonic
2025-06-21T03:31:56.105009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130 6
 
0.7%
70 5
 
0.6%
140 5
 
0.6%
200 5
 
0.6%
290 5
 
0.6%
590 4
 
0.5%
350 4
 
0.5%
480 4
 
0.5%
170 4
 
0.5%
150 4
 
0.5%
Other values (487) 608
69.9%
(Missing) 216
 
24.8%
ValueCountFrequency (%)
38 1
 
0.1%
44 2
0.2%
45 1
 
0.1%
49 1
 
0.1%
50 3
0.3%
51 1
 
0.1%
53 1
 
0.1%
54 1
 
0.1%
55 1
 
0.1%
60 2
0.2%
ValueCountFrequency (%)
54626 1
0.1%
13722 1
0.1%
12520 1
0.1%
10140 1
0.1%
10000 1
0.1%
9450 1
0.1%
9410 1
0.1%
8595 1
0.1%
8021 1
0.1%
6519 1
0.1%

sewer network connection
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.4%
Missing398
Missing (%)45.7%
Memory size112.3 KiB
1.0
461 
0.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1416
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 461
53.0%
0.0 11
 
1.3%
(Missing) 398
45.7%

Length

2025-06-21T03:31:56.135070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T03:31:56.241902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 461
97.7%
0.0 11
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 483
34.1%
. 472
33.3%
1 461
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 483
34.1%
. 472
33.3%
1 461
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 483
34.1%
. 472
33.3%
1 461
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 483
34.1%
. 472
33.3%
1 461
32.6%

proceedings for breach of planning regulations
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)0.4%
Missing327
Missing (%)37.6%
Memory size163.6 KiB
0.0
542 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1629
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 542
62.3%
1.0 1
 
0.1%
(Missing) 327
37.6%

Length

2025-06-21T03:31:56.261174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T03:31:56.276306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 542
99.8%
1.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1085
66.6%
. 543
33.3%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1629
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1085
66.6%
. 543
33.3%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1629
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1085
66.6%
. 543
33.3%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1629
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1085
66.6%
. 543
33.3%
1 1
 
0.1%

designated land use
Real number (ℝ)

Missing 

Distinct14
Distinct (%)2.2%
Missing239
Missing (%)27.5%
Infinite0
Infinite (%)0.0%
Mean1.9144216
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.1 KiB
2025-06-21T03:31:56.292905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile7
Maximum14
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9401271
Coefficient of variation (CV)1.0134273
Kurtosis9.1048754
Mean1.9144216
Median Absolute Deviation (MAD)0
Skewness2.8893703
Sum1208
Variance3.7640933
MonotonicityNot monotonic
2025-06-21T03:31:56.316254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 418
48.0%
2 121
 
13.9%
5 25
 
2.9%
3 17
 
2.0%
7 14
 
1.6%
6 12
 
1.4%
8 8
 
0.9%
4 5
 
0.6%
9 3
 
0.3%
10 3
 
0.3%
Other values (4) 5
 
0.6%
(Missing) 239
27.5%
ValueCountFrequency (%)
1 418
48.0%
2 121
 
13.9%
3 17
 
2.0%
4 5
 
0.6%
5 25
 
2.9%
6 12
 
1.4%
7 14
 
1.6%
8 8
 
0.9%
9 3
 
0.3%
10 3
 
0.3%
ValueCountFrequency (%)
14 1
 
0.1%
13 1
 
0.1%
12 1
 
0.1%
11 2
 
0.2%
10 3
 
0.3%
9 3
 
0.3%
8 8
 
0.9%
7 14
1.6%
6 12
1.4%
5 25
2.9%

double glazing
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)0.4%
Missing305
Missing (%)35.1%
Memory size163.5 KiB
1.0
543 
0.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1695
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 543
62.4%
0.0 22
 
2.5%
(Missing) 305
35.1%

Length

2025-06-21T03:31:56.342880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T03:31:56.360439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 543
96.1%
0.0 22
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 587
34.6%
. 565
33.3%
1 543
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1695
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 587
34.6%
. 565
33.3%
1 543
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1695
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 587
34.6%
. 565
33.3%
1 543
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1695
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 587
34.6%
. 565
33.3%
1 543
32.0%

Interactions

2025-06-21T03:31:54.174505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.198016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.464649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.758124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.392995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.626675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.987365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:53.523157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:54.224278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.248902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.491991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.786631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.422233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.659562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:53.023181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:53.579043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:54.260764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.281847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.525928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.815578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.451147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.690374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:53.058583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:53.654663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:54.293896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.315059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.561594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.233332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.480650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.742061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:53.142028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:53.739269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:54.352884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.342597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.605619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.265694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.507323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.795000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:53.254028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:53.979179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:54.385036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.378242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.642002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.298929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.538754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.841945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:53.292099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:54.047816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:54.414535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.408685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.682731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.331657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.567529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.899583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:53.380285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:54.085575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:54.447259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.436678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:51.722631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.365028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.596645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:52.944230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:53.434570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T03:31:54.128389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-21T03:31:56.382956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
asbestos certificatebathroomsbedroomsbuilding conditiondesignated land usedouble glazingenergy classg-scoreinspection report of the electrical installationliving areanon-flood zoneplanning permission obtainedpossible priority purchase rightpriceprimary energy consumptionproceedings for breach of planning regulationssewer network connectionshared buildingsubdivision permitsurface of the plottoilets
asbestos certificate1.0000.0630.1390.3540.1690.0580.3440.0000.1310.0930.2550.1160.0000.2701.0000.0000.0790.0000.2370.0000.078
bathrooms0.0631.0000.6710.0440.0800.080-0.0810.0310.0000.3360.0000.0540.0600.349-0.0860.0000.0300.1940.0000.1550.401
bedrooms0.1390.6711.0000.0520.0000.0000.0340.0000.1050.6140.0450.0000.0000.2400.0000.0000.0000.1740.1120.1990.574
building condition0.3540.0440.0521.0000.0660.1300.3170.0410.2580.0000.1060.1470.0160.0950.0300.0000.0730.0390.0990.0000.080
designated land use0.1690.0800.0000.0661.0000.201-0.0240.0000.0000.0320.1260.0600.1320.2370.0010.0000.0000.0000.2130.1650.094
double glazing0.0580.0800.0000.1300.2011.0000.1570.0000.1550.0000.0000.0000.0000.0000.0930.0000.0000.0000.0690.0000.000
energy class0.344-0.0810.0340.317-0.0240.1571.0000.0540.1340.0390.0640.1680.000-0.3490.9830.0000.1010.0320.2750.131-0.126
g-score0.0000.0310.0000.0410.0000.0000.0541.0000.0000.0570.0260.0000.0360.0000.0000.0000.0000.0000.1440.0520.000
inspection report of the electrical installation0.1310.0000.1050.2580.0000.1550.1340.0001.0000.0940.0810.0760.0000.0311.0000.0000.3060.0000.0810.0000.000
living area0.0930.3360.6140.0000.0320.0000.0390.0570.0941.0000.0000.0740.0000.5770.0360.0000.0000.0130.0000.4760.564
non-flood zone0.2550.0000.0450.1060.1260.0000.0640.0260.0810.0001.0000.3560.0760.2190.0000.0000.0040.0500.0890.0000.000
planning permission obtained0.1160.0540.0000.1470.0600.0000.1680.0000.0760.0740.3561.0000.0610.0000.0000.0000.0000.0000.2190.0720.035
possible priority purchase right0.0000.0600.0000.0160.1320.0000.0000.0360.0000.0000.0760.0611.0000.0960.0000.0020.0000.0000.0000.0000.055
price0.2700.3490.2400.0950.2370.000-0.3490.0000.0310.5770.2190.0000.0961.000-0.3220.0000.0000.1810.1720.5350.530
primary energy consumption1.000-0.0860.0000.0300.0010.0930.9830.0001.0000.0360.0000.0000.000-0.3221.0000.0001.0000.0000.0000.132-0.128
proceedings for breach of planning regulations0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0001.0000.0000.0000.0000.0000.000
sewer network connection0.0790.0300.0000.0730.0000.0000.1010.0000.3060.0000.0040.0000.0000.0001.0000.0001.0000.0000.0000.0000.000
shared building0.0000.1940.1740.0390.0000.0000.0320.0000.0000.0130.0500.0000.0000.1810.0000.0000.0001.0000.0000.0000.228
subdivision permit0.2370.0000.1120.0990.2130.0690.2750.1440.0810.0000.0890.2190.0000.1720.0000.0000.0000.0001.0000.0000.000
surface of the plot0.0000.1550.1990.0000.1650.0000.1310.0520.0000.4760.0000.0720.0000.5350.1320.0000.0000.0000.0001.0000.250
toilets0.0780.4010.5740.0800.0940.000-0.1260.0000.0000.5640.0000.0350.0550.530-0.1280.0000.0000.2280.0000.2501.000

Missing values

2025-06-21T03:31:54.507703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-21T03:31:54.835064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-21T03:31:54.935640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

addresspriceconstruction yearbuilding conditionasbestos certificateliving areabedroomsbathroomstoiletsprimary energy consumptionenergy classinspection report of the electrical installationplanning permission obtainedsubdivision permitpossible priority purchase rightnon-flood zoneg-scoreshared buildingsurface of the plotsewer network connectionproceedings for breach of planning regulationsdesignated land usedouble glazing
uuid
bff0933c-8706-450e-be72-df1c836eb396vrijdagmarkt 61 box 305 9000 — gent765000.02015.01.00.0171.02.01.02.0102.04.01.010.01.011.00.0NaNNaNNaNNaNNaN
48103edf-d945-4052-a349-31a1bdee8300gitsestraat 545 8800 — roeselare321477.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaN0NaNNaNNaNNaNNaNNaNNaN
9c358ca3-4960-4808-91d6-0cf3b053455cbruggestraat 143 8730 — beernem980000.01974.03.01.0NaN5.01.02.0231.05.01.01NaNNaN11.01.05665.01.00.01.0NaN
71709641-06d1-4b17-a633-eef3ce865c5a8670 — koksijde ask for the exact address445000.02022.01.0NaN85.02.01.01.0NaN3.0NaN10.01.01NaN0.0NaNNaN0.0NaN1.0
2f1f319c-c755-4fdb-b4de-f9197de6047dhamerstraat 75 9000 — gent330000.01899.03.0NaN118.03.01.01.0167.04.01.00NaNNaN11.00.044.01.0NaNNaN1.0
ae248bd9-f94b-40d8-9466-d73d2ae31c56zuidburgweg 107 8630 — veurne534608.02023.03.00.0NaN3.02.02.0NaNNaNNaN0NaNNaN0NaN0.0NaNNaNNaNNaNNaN
5996b9a4-f4f5-432c-baea-f8ac497bfa53stationsstraat 125 box 1 9950 — lievegem179000.01968.03.00.087.02.01.01.0155.04.0NaN10.00.014.00.0NaNNaN0.0NaNNaN
9dad22f3-ef89-484f-87b2-0e6eb2ebd63b9940 — evergem ask for the exact address690099.0NaN3.00.0174.03.01.02.0NaNNaNNaN0NaNNaN0NaN0.01419.01.0NaN2.0NaN
cfa415dd-888d-435e-914e-92c28b246a9c9420 — erpe-mere ask for the exact address668514.0NaN3.00.0213.03.02.02.0NaNNaN1.00NaNNaN0NaN0.0446.01.0NaN2.01.0
914bfbd3-46a2-4b84-93ec-18b08330e9b8vossestraat 46 9890 — gavere165000.0NaN4.0NaN114.02.01.01.0484.07.0NaN00.00.011.00.0356.0NaN0.03.01.0
addresspriceconstruction yearbuilding conditionasbestos certificateliving areabedroomsbathroomstoiletsprimary energy consumptionenergy classinspection report of the electrical installationplanning permission obtainedsubdivision permitpossible priority purchase rightnon-flood zoneg-scoreshared buildingsurface of the plotsewer network connectionproceedings for breach of planning regulationsdesignated land usedouble glazing
uuid
7cd59daf-450f-4f37-bbd1-b6a8452595acbosstraat 2b 9770 — kruisem730000.02000.03.01.0301.04.01.02.0213.05.01.011.00.011.00.0950.01.00.02.0NaN
32cced71-ba29-4272-9364-f2f3b49a4479slotstraat 20 9300 — aalst269000.01934.02.0NaN135.03.01.01.0360.06.01.010.00.00NaN0.0100.01.00.01.01.0
0242bd11-9e77-49ca-bcf9-e965ae3985d4achterstraat 79 box 9 9800 — deinze289000.02014.0NaN0.092.02.01.02.0100.03.01.011.0NaN11.00.0NaNNaN0.0NaN1.0
de0e2d44-c0f2-447e-84bf-ff7a47ff8fbc9810 — nazareth ask for the exact address794867.0NaN3.00.0188.03.01.02.0NaNNaN1.00NaNNaN0NaN0.01175.01.0NaN2.01.0
29fb7502-7b99-4526-ae0b-2e10bd5731c1omgangstraat 47 9750 — kruisem398000.02005.03.01.0157.03.01.02.095.03.01.011.00.001.00.01138.01.0NaN1.01.0
006e0e56-2bbb-454f-be7a-db4d6c547e63sint-vincentiusstraat 18 9100 — sint-niklaas365000.0NaN1.0NaN165.04.02.03.0188.04.0NaN10.00.013.01.0140.01.00.01.01.0
7ccef155-0c32-4bed-b26f-e6c63e8b16c0heidestraat 22 9050 — gentbrugge275000.01918.03.01.075.01.01.01.0199.04.0NaN10.01.011.00.0NaNNaN0.01.01.0
11bc94bd-1f30-4144-b340-b63c110a767bbrouwerijstraat 4 9940 — evergem279000.01923.05.00.0150.03.02.01.0385.06.01.00NaN0.011.00.0233.0NaNNaN1.01.0
7ab01535-9fbd-42b9-9ee0-236f2945259e9970 — kaprijke ask for the exact address586113.0NaN3.00.0209.03.01.02.0NaNNaNNaN0NaNNaN0NaN0.0618.01.0NaN2.0NaN
6a3ae3f6-0901-46fa-836e-22e06d444738boekweitstraat 12 8600 — diksmuide395000.01998.0NaNNaN135.03.01.0NaN109.04.01.010.00.011.00.0540.01.00.02.01.0

Duplicate rows

Most frequently occurring

addresspriceconstruction yearbuilding conditionasbestos certificateliving areabedroomsbathroomstoiletsprimary energy consumptionenergy classinspection report of the electrical installationplanning permission obtainedsubdivision permitpossible priority purchase rightnon-flood zoneg-scoreshared buildingsurface of the plotsewer network connectionproceedings for breach of planning regulationsdesignated land usedouble glazing# duplicates
9polderstraat 71 8670 — oostduinkerke525000.01933.03.00.0NaN3.01.02.038.03.01.010.01.012.00.0711.0NaN0.01.01.03
09050 — gent ask for the exact address482000.02024.0NaN0.099.03.01.0NaNNaNNaN1.011.01.01NaN0.0NaNNaN0.0NaN1.02
1bosstraat 15 9450 — haaltert299000.01951.04.01.0180.04.01.01.0995.08.01.010.00.013.00.04384.01.00.01.0NaN2
2daningsdreef 2 9840 — nazareth-de pinte672000.01977.01.01.0211.04.01.02.097.03.01.011.00.011.00.0616.01.00.01.01.02
3de brownestraat 32 9120 — beveren460000.01953.03.01.0180.04.01.02.0175.04.01.010.00.011.00.0333.01.00.01.01.02
4gentsestraat 58 9420 — burst544000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1NaNNaN12.0NaNNaNNaN0.0NaNNaN2
5jozef hebbelynckstraat 14 9820 — merelbeke489000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaN0NaNNaNNaNNaNNaNNaNNaN2
6keizersplein 47-49 9300 — aalst1100000.01845.03.00.0992.05.04.0NaNNaNNaNNaN10.00.014.00.0590.0NaN0.05.0NaN2
7mechelsesteenweg 119 9200 — dendermonde579000.0NaN4.00.0251.03.01.02.0532.08.01.000.00.011.00.04143.01.0NaN1.01.02
8pelgrim 40 9860 — oosterzele625000.01995.01.00.0230.04.01.02.0242.05.0NaN1NaN0.011.00.01100.01.0NaN1.01.02